class_docstr: Predicate-Object Frequency Weight edge-centric sampling strategy which
  prioritizes walks containing edges with the highest degree of (predicate,
  object) relations. The degree of a such relation being defined by the
  number of occurences that a (predicate, object) relation appears in a
  Knowledge Graph.
  
  Attributes:
      _counts: The counter for vertices.
          Defaults to defaultdict.
      _is_support_remote: True if the sampling strategy can be used with a
          remote Knowledge Graph, False Otherwise
          Defaults to False.
      _random_state: The random state to use to keep random determinism with
          the sampling strategy.
          Defaults to None.
      _vertices_deg: The degree of the vertices.
          Defaults to {}.
      _visited: Tags vertices that appear at the max depth or of which all
          their children are tagged.
          Defaults to set.
      inverse: True if the inverse algorithm must be used, False otherwise.
          Defaults to False.
      split: True if the split algorithm must be used, False otherwise.
          Defaults to False.
class_docstr_tok: ["Predicate-Object","Frequency","Weight","edge-centric","sampling","strategy","which","prioritizes","walks","containing","edges","with","the","highest","degree","of","(","predicate",",","object",")","relations",".","The","degree","of","a","such","relation","being","defined","by","the","number","of","occurences","that","a","(","predicate",",","object",")","relation","appears","in","a","Knowledge","Graph",".","Attributes",":","_counts",":","The","counter","for","vertices",".","Defaults","to","defaultdict",".","_is_support_remote",":","True","if","the","sampling","strategy","can","be","used","with","a","remote","Knowledge","Graph",",","False","Otherwise","Defaults","to","False",".","_random_state",":","The","random","state","to","use","to","keep","random","determinism","with","the","sampling","strategy",".","Defaults","to","None",".","_vertices_deg",":","The","degree","of","the","vertices",".","Defaults","to","{","}",".","_visited",":","Tags","vertices","that","appear","at","the","max","depth","or","of","which","all","their","children","are","tagged",".","Defaults","to","set",".","inverse",":","True","if","the","inverse","algorithm","must","be","used",",","False","otherwise",".","Defaults","to","False",".","split",":","True","if","the","split","algorithm","must","be","used",",","False","otherwise",".","Defaults","to","False","."]
docstr: Gets the weight of a hop in the Knowledge Graph.
  
  Args:
      hop: The hop of a vertex in a (predicate, object) form to get the
          weight.
  
  Returns:
      The weight of a given hop.
  
  Raises:
      ValueError: If there is an attempt to access the weight of a hop
          without the sampling strategy having been trained.
docstr_tok: ["Gets","the","weight","of","a","hop","in","the","Knowledge","Graph",".","Args",":","hop",":","The","hop","of","a","vertex","in","a","(","predicate",",","object",")","form","to","get","the","weight",".","Returns",":","The","weight","of","a","given","hop",".","Raises",":","ValueError",":","If","there","is","an","attempt","to","access","the","weight","of","a","hop","without","the","sampling","strategy","having","been","trained","."]
code: def get_weight(self, hop: Hop) -> int:
      """Gets the weight of a hop in the Knowledge Graph.
  
      Args:
          hop: The hop of a vertex in a (predicate, object) form to get the
              weight.
  
      Returns:
          The weight of a given hop.
  
      Raises:
          ValueError: If there is an attempt to access the weight of a hop
              without the sampling strategy having been trained.
  
      """
      if not self._counts:
          raise ValueError(
              "You must call the `fit(kg)` method before get the weight of"
              + " a hop."
          )
      return self._counts[(hop[0].name, hop[1].name)]
  
file_path: pyRDF2Vec/pyrdf2vec/samplers/frequency.py
imports: from collections import defaultdict
  from typing import DefaultDict, Tuple
  import attr
  from pyrdf2vec.graphs import KG
  from pyrdf2vec.samplers import Sampler
  from pyrdf2vec.typings import Hop
label_desc: Use image node_id 2 for calling the ObjPredFreqSampler obj's underlying member method code with example usage: obj.get_weight(hop) and returns: self